Recent trends have shown that there is more and more user-reliance on search engines to not only provide search results in response to the user's query, but to assist the user in satisfying their intents during a search session. Some ways search engines currently assist users involve correcting misspellings in queries, expanding on the subject matter of the query to generate a more diverse set of search results, and offering alternative queries to the user. This last assistive feature of search engines may also offer suggested websites in addition to alternative queries.
Specifically, conventional search-engine relies on recommendation technology to provide rudimentary mechanism(s) that present a suggested website, which the user may be interested in visiting, based on a similarity between the user's current site, or the user's current query, and another website. That is, the website being suggested to the user is based on just the single, most-recently opened website. However, this approach neglects any prior websites visited by the user during a search session and fails to take into account browsing history of other users, which may help more accurately predict the user's true interests. Accordingly, predictive model(s) that evaluate a broader set of inputs (e.g., latest N-number of visited websites and other criteria) and perform in-depth analyses using those inputs would more effectively target users' search intents.